Spatially adaptive subject level analyses improve random effects fMRI group studies

S. Badillo, T. Vincent, P. Ciuciu
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Abstract

Inter-subject analysis of functional Magnetic Resonance Imaging (fMRI) data relies on single intra-subject studies, which are usually conducted using a massively univariate approach. In this paper, we investigate the impact of an improved intra-subject analysis on group studies. Our approach is based on the use of Adaptive Spatial Mixture Models within a joint detection-estimation (JDE) framework [1]. In this setting, spatial variability is achieved at a regional scale by the explicit characterization of the hemodynamic filter and at the voxel scale by an adaptive spatial correlation model between condition-specific effects. For the group statistics, we conducted several Random effect analyses (RFX) which relied either on SPM or JDE intra-subject analyses. We performed a comparative study on two different real datasets involving the same paradigm and the same 15 subjects but eliciting different noise levels by varying the acceleration factor (R=2 and R=4) in parallel MRI acquisition. We show that brain activations appear more spatially resolved using JDE instead of SPM and that a better sensitivity is achieved. Moreover, the JDE framework provides more robust detection performance by maintaining satisfying results on our most noisy real dataset.
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空间适应性受试者水平分析改善了fMRI组研究的随机效应
功能磁共振成像(fMRI)数据的主体间分析依赖于单个主体内研究,通常使用大量单变量方法进行。在本文中,我们研究了改进的受试者内分析对群体研究的影响。我们的方法是基于在联合检测-估计(JDE)框架[1]中使用自适应空间混合模型。在这种情况下,空间变异性在区域尺度上通过明确表征血流动力学过滤器来实现,在体素尺度上通过条件特异性效应之间的自适应空间相关模型来实现。对于组统计,我们进行了几次随机效应分析(RFX),这些分析依赖于SPM或JDE的受试者内部分析。我们对两个不同的真实数据集进行了比较研究,这些数据集涉及相同的范式和相同的15个受试者,但通过改变加速因子(R=2和R=4)在并行MRI采集中引起不同的噪声水平。我们表明,使用JDE而不是SPM,大脑激活在空间上表现得更加清晰,并且获得了更好的灵敏度。此外,通过在最嘈杂的真实数据集上保持令人满意的结果,JDE框架提供了更健壮的检测性能。
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